Natural language processing in AI-assisted programming leads to challenges in user intent specification, often necessitating multiple refinements. Participants encountered issues with ambiguity and complexity, complicating their interactions with code-generating models. End-user programmers frequently lack essential computational thinking skills, affecting their ability to decompose problems and utilize AI tools effectively. Additionally, existing metaphors, such as AI assistance as search or compilation, fall short in describing the unique interaction dynamics when programming with AI assistance. This gap indicates a need for improved frameworks and user education regarding AI capabilities.
Participants frequently faced challenges in specifying their intent clearly during data analysis tasks, often having to refine their natural language inputs multiple times before achieving desired results.
Underspecified and ambiguous natural language utterances problematic for effective interaction with AI models highlight the need for improved communication about system capabilities and interpretations.
End-user programmers often struggle with essential computational thinking skills, which are critical for effective problem decomposition and formulation when using AI programming assistance.
The existing metaphors for AI-assisted programming, like search and pair programming, fail to encapsulate the distinct nature of programming in an increasingly automated environment.
#ai-assistance #programming-tools #natural-language-processing #computational-thinking #code-generation
Collection
[
|
...
]